Classified data often manifest a salt-and-pepper appearance due
to the inherent spectral variability encountered by a classification when applied on a
pixel-by pixel basis (Lillesand and Kiefer, 1994). It is often desirable to “smooth”
the classified output to show only the dominant (presumably correct) classification.

One means of classification smoothing involves the application of a
majority filter. In such operations a moving windows is pass through the classified pixel
in the window is not the majority class, its identity is changed to the majority class. If
there is no majority class in the window, the identity of the center pixel is not changed.
As the windows progresses through the data set, the original class code are continually
used, not the labels as modified from the previous window position. (Eastman, 1995)

Majority filters can also incorporate some from of class and/or spatial
weighting function. Data may also be smoothed mote than once. Certain algorithms can
preserve the boundaries between land cover regions and also involve a user-specified
minimum area for any given land cover type that will be maintained in the smooth output
(Lillesand and Kiefer, 1994).

Ground truth or field survey is done in order to observe and collect
information about the actual condition on the ground at a test site and determine the
relationship between remotely sensed data and the object to be observed. It is recommended
to have a ground truth at the same time of data acquisition, or at least within the time
that the environmental condition does not change.

Classification accuracy assessment is a general term for comparing the
classification to geographical data that are assumed to be true to determine the accuracy
of the classification process. Usually, the assumed true data are derived from ground
truth. It is usually not practical to ground truth or otherwise test every pixel of a
classified image. Therefore a set of reference pixels is usually used. Reference pixels
are points on the classified image for which actual data are(will be) known. The reference
pixel are randomly select.(Congalton , 1991)